





'''simple docstring'''






from __future__ import annotations

__UpperCamelCase					    =	[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	) ->				tuple[list[list[int]], list[list[int]]]:



			"""simple docstring"""


			__snake_case					:      List[str]							      =     [
			    [0 for col in range(len(grid[0] ) )] for row in range(len(_lowerCamelCase ) )
			]  # the reference grid
			__snake_case					:      Tuple							      =     1
			__snake_case					:      List[str]							      =     [
			    [0 for col in range(len(grid[0] ) )] for row in range(len(_lowerCamelCase ) )
			]  # the action grid

			__snake_case					:      List[str]							      =     init[0]
			__snake_case					:      str							      =     init[1]
			__snake_case					:      int							      =     0
			__snake_case					:      int							      =     g + heuristic[x][y]  # cost from starting cell to destination cell
			__snake_case					:      List[str]							      =     [[f, g, x, y]]

			__snake_case					:      Any							      =     False  # flag that is set when search is complete
			__snake_case					:      int							      =     False  # flag set if we can't find expand

			while not found and not resign:
						if len(_lowerCamelCase ) == 0:
									raise ValueError("""Algorithm is unable to find solution""" )
						else:  # to choose the least costliest action so as to move closer to the goal
									cell.sort()
									cell.reverse()
									__snake_case					:      Tuple							      =     cell.pop()
									__snake_case					:      Optional[int]							      =     next_cell[2]
									__snake_case					:      List[Any]							      =     next_cell[3]
									__snake_case					:      int							      =     next_cell[1]

									if x == goal[0] and y == goal[1]:
												__snake_case					:      Optional[Any]							      =     True
									else:
												for i in range(len(_lowerCamelCase ) ):  # to try out different valid actions
															__snake_case					:      Union[str, Any]							      =     x + DIRECTIONS[i][0]
															__snake_case					:      str							      =     y + DIRECTIONS[i][1]
															if xa >= 0 and xa < len(_lowerCamelCase ) and ya >= 0 and ya < len(grid[0] ):
																		if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																					__snake_case					:      str							      =     g + cost
																					__snake_case					:      Tuple							      =     ga + heuristic[xa][ya]
																					cell.append([fa, ga, xa, ya] )
																					__snake_case					:      List[str]							      =     1
																					__snake_case					:      Optional[int]							      =     i
			__snake_case					:      List[str]							      =     []
			__snake_case					:      Optional[int]							      =     goal[0]
			__snake_case					:      List[Any]							      =     goal[1]
			invpath.append([x, y] )  # we get the reverse path from here
			while x != init[0] or y != init[1]:
						__snake_case					:      Dict							      =     x - DIRECTIONS[action[x][y]][0]
						__snake_case					:      int							      =     y - DIRECTIONS[action[x][y]][1]
						__snake_case					:      Optional[int]							      =     xa
						__snake_case					:      int							      =     ya
						invpath.append([x, y] )

			__snake_case					:      Optional[int]							      =     []
			for i in range(len(_lowerCamelCase ) ):
						path.append(invpath[len(_lowerCamelCase ) - 1 - i] )
			return path, action


if __name__ == "__main__":
	__UpperCamelCase					    =	[
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
	    [0, 1, 0, 0, 0, 0],
	    [0, 1, 0, 0, 1, 0],
	    [0, 0, 0, 0, 1, 0],
	]

	__UpperCamelCase					    =	[0, 0]
	# all coordinates are given in format [y,x]
	__UpperCamelCase					    =	[len(grid) - 1, len(grid[0]) - 1]
	__UpperCamelCase					    =	1

	# the cost map which pushes the path closer to the goal
	__UpperCamelCase					    =	[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
	for i in range(len(grid)):
		for j in range(len(grid[0])):
			__UpperCamelCase					    =	abs(i - goal[0]) + abs(j - goal[1])
			if grid[i][j] == 1:
				# added extra penalty in the heuristic map
				__UpperCamelCase					    =	99

	__UpperCamelCase    ,		__UpperCamelCase					    =	search(grid, init, goal, cost, heuristic)

	print("ACTION MAP")
	for i in range(len(action)):
		print(action[i])

	for i in range(len(path)):
		print(path[i])







'''simple docstring'''



import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
							from sagemaker import Session, TrainingJobAnalytics
							from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'     ,					'False'      )      ) is not True     ,					reason='Skipping test because should only be run when releasing minor transformers version'     ,					)
@pytest.mark.usefixtures('sm_env'      )
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue_model_parallelism.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 1_6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 1_6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]      )
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													if self.framework == "pytorch":
																				subprocess.run(
																				    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()					,     encoding='''utf-8'''					,     check=lowerCAmelCase__					,     )
													assert hasattr(self					,     '''env'''					)







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													# configuration for running training on smdistributed Model Parallel
													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''enabled''': True,
													    '''processes_per_host''': 8,
													}
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      {
													    '''enabled''': True,
													    '''parameters''': {
													        '''microbatches''': 4,
													        '''placement_strategy''': '''spread''',
													        '''pipeline''': '''interleaved''',
													        '''optimize''': '''speed''',
													        '''partitions''': 4,
													        '''ddp''': True,
													    },
													}

													SCREAMING_SNAKE_CASE       :   str         =      {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
													# creates estimator
													return HuggingFace(
													    entry_point=self.script					,     source_dir=self.env.test_path					,     role=self.env.role					,     image_uri=self.env.image_uri					,     base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""					,     instance_count=lowerCAmelCase__					,     instance_type=self.instance_type					,     debugger_hook_config=lowerCAmelCase__					,     hyperparameters={
													        **self.env.hyperparameters,
													        '''model_name_or_path''': self.model_name_or_path,
													        '''max_steps''': 5_00,
													    }					,     metric_definitions=self.env.metric_definitions					,     distribution=lowerCAmelCase__					,     py_version='''py36'''					,     )







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													TrainingJobAnalytics(lowerCAmelCase__					).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""					)







						@parameterized.expand([(1,)]					)
						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													# create estimator
													SCREAMING_SNAKE_CASE       :   Any         =      self.create_estimator(lowerCAmelCase__					)

													# run training
													estimator.fit()

													# result dataframe
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

													# extract kpis
													SCREAMING_SNAKE_CASE       :   int         =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']					)
													# get train time from SageMaker job, this includes starting, preprocessing, stopping
													SCREAMING_SNAKE_CASE       :   int         =      (
													    Session().describe_training_job(estimator.latest_training_job.name					).get('''TrainingTimeInSeconds'''					,     99_99_99					)
													)

													# assert kpis
													assert train_runtime <= self.results["train_runtime"]
													assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy					)
													assert all(t <= self.results['''eval_loss'''] for t in eval_loss					)

													# dump tests result into json file to share in PR
													with open(F"""{estimator.latest_training_job.name}.json"""					,     '''w'''					) as outfile:
																				json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}					,     lowerCAmelCase__					)




